{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1、任务描述\n",
    "请在 Pima Indians Diabetes Data Set（皮马印第安人糖尿病数据集）进行分类器练\n",
    "习。\n",
    "需要提交代码文件，并给出必要的结果解释。\n",
    "1)  训练数据和测试数据分割（随机选择 20%的数据作为测试集）；（10 分）\n",
    "2)  适当的特征工程（及数据探索）;（10 分）\n",
    "3)  Logistic 回归，并选择最佳的正则函数（L1/L2）及正则参数；（30 分）\n",
    "4)  线性 SVM，并选择最佳正则参数，比较与 Logistic 回归的性能，简单说明原因。（20 分）\n",
    "5)  RBF 核的 SVM，并选择最佳的超参数（正则参数、RBF 核函数宽度）；（30 分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 首先 import 必要的模块\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "#竞赛的评价指标为logloss\n",
    "from sklearn.metrics import log_loss  \n",
    "from sklearn import metrics\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import pylab  # 显示大小使用\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2、读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "dpath = './data/'\n",
    "train = pd.read_csv(dpath +\"diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3、数据探索&特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 糖尿病发生率\n",
    "pylab.rcParams['figure.figsize'] = (15.0, 8.0) # 显示大小\n",
    "sns.countplot(train.Outcome);\n",
    "plt.xlabel('Outcome');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 怀孕次数与糖尿病发病率的关系\n",
    "sns.violinplot(data=train,x='Outcome',y='Pregnancies')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 口服葡萄糖耐受试验中，2 小时的血浆葡萄糖浓度与糖尿病发病率的关系\n",
    "sns.violinplot(x=train.Outcome,y=train.Glucose)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 舒张压（mm Hg）与糖尿病发病率的关系\n",
    "sns.scatterplot(x=train.BloodPressure,y=train.Outcome)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去除舒张压（mm Hg）中的离群点\n",
    "train=train[train.BloodPressure>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 三头肌皮肤褶层厚度（mm）与糖尿病发病率的关系\n",
    "sns.scatterplot(x=train.SkinThickness,y=train.Outcome)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去除三头肌皮肤褶层厚度（mm）中的离群点\n",
    "train=train[train.SkinThickness<70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2 小时血清胰岛素含量（μU/ ml）与糖尿病发病率的关系\n",
    "sns.boxplot(x=train.Outcome,y=train.Insulin)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去除2 小时血清胰岛素含量中的离群点\n",
    "train=train[train.Insulin<600]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 体重指数与糖尿病发病率的关系\n",
    "sns.boxplot(x=train.Outcome,y=train.BMI)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去除体重指数中的离群点\n",
    "train=train[train.BMI<60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 糖尿病家族史与糖尿病发病率的关系\n",
    "sns.boxplot(x=train.Outcome,y=train.DiabetesPedigreeFunction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去除糖尿病家族史中的离群点\n",
    "train=train[train.DiabetesPedigreeFunction<2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JWjs9/bHbzQxAodi6datSqZRSNeOH9fhk9ThJ0oYNG9yMBQAoMJnS1nSGHTQz2EkTcE+2dtEE4LL169fLFAWVqhw7rMfb0krZUK3Wr1/vcjIAQCE51xEJGWNDKRlR8AA3UPAAn3h93TolKpukQNGwfydRPV7btm1XNBp1MRkAoJC0tLTIaLDEnU1pkTQ6RMED3EDBA3ygtbVVx9valBzm8syMZM14JZMJbdu2zaVkAIBC09raqrpyqeQ87zc2lcXV0nwkO6GAAkLBA3wgczzChRa8VFWjTCDIMk0AwIhpPnJYY8vi531cY7mjlpYWdnMGRhgFD/CB9evXS2VVsqXVF/aLgaASlWO1bj0brQAALp21Vq2trWoMnX0HzYzG8pTCkai6u9lvDxhJFDwgzyWTSW3avFnxqnE664my5/r9mvFqbWlWe3u7C+kAAIXk5MmTCkei59xgJSPzmKNHj7odCygoFDwgz+3atUuxaFSp6gtbnpmR+T2OSwAAXKrMEQkXUvDYaAUYWRQ8IM9t3LhRMkbJ6uEdcP5OTqhWprRi6D4+AAAu1nCOSMioK3MUDHDYOTDSKHhAntu4cZOcijopWHpxFzBG8cpGbdy0WY5z/nsmAAA4m9bWVgUDg+XtfAJGaghZCh4wwih4QB4Lh8Pas3ePElUXN3uXkawep77eHh0+fHhkggEAClJra6sayq0Cw7wlfGwoodaWZndDAQWGggfksW3btslJpZSqHndJ10mlC+KmTZtGIhYAoEC1NB/R2LLEsB/fGErp6NFjrCABRhAFD8hjW7dulQJFSlU2XNJ1bGmlFKoZvB4AABfBcRwdO9amxtD577/LaCxPKZ5IsJMzMIIoeEAe27Ztm1IVdVIgeMnXSlQ0aNv27byLCgC4KO3t7YonEsPaYCUj81juwwNGDgUPyFPRaFT79u9XsmLsiFwvWTlW/X19bFcNALgomZLWdBEFj+ceYORQ8IA8tXv37sH776pGpuBlrrN9+/YRuR4AoLA0Nw9ulnIhM3i1JVZlQUPBA0YQBQ/IUzt37pSkS77/LsOWVsuUhLRjx44RuR4AoLC0tLQoVGxUU2KH/TvGSE3lyaFyCODSUfCAPLV7924pVHPx59+9kzFKhOq0a/fukbkeAKCgNDcfUVMoKTPMIxIymkJJNR857EomoBBR8IA8tWv3biVCdSN6zVRFnVpbWhQOh0f0ugAA/2s+fFhN5cM/IiGjqTyl9o5ORSIRF1IBhYeCB+Shzs5OnezuHtxBcwSlKupkrdW+fftG9LoAAH/r7+9XR1e3xl3A/XcZ4yoGf4dlmsDIoOABeShTwJyKMSN6XSddGPfv3z+i1wUA+NuRI0ckSRMqL7zgTahISpIOHz48kpGAgkXBA/LQoUOHJEmp0OgRva4tDsmUhIauDwDAcGSeNyZUXHjBawg5Kg6I5x5ghFDwgDx06NAhmbIqKVgy4tdOlNXqwJtvjvh1AQD+dfDgQZUGpTFlzgX/blFAGlfh6E2ee4ARQcED8tCBA28qUVrryrWd0GgdPnRYjnPhT9IAgMK0f98+TapIKnCBO2hmTKqIa/++vbJ2+EcsADgzCh6QZ1KplFpaW+SE3Cp4tYrHYzpx4oQr1wcA+IvjODpwYL8mVyYv+hqTq1I61dOrrq6uEUwGFCYKHpBn2tvblUom5ZRVu3J9p3TwukePHnXl+gAAfzl27Jgi0ZgmV11KwRv8XXZxBi4dBQ/IM5nilSliIy1THFtbW125PgDAX3bt2iVJuqz64gvelKrBA9L37NkzUrGAgkXBA/JMpni5NYNni8tlioIUPADAsOzatUtlQaPxF7GDZkZpkTSp0tHOnTtGMBlQmCh4QJ5pa2uTAkWyxeXuDGCMnNLqwXEAADiPnTt26LKq+EVvsJJxeXVcu3ftUip18UURAAUPyDvt7e1SaaVkLvGZ9BxSxeU6fpxNVgAA5xYOh/XmwYOaVpO45GtNr04oHIlyHh5wiSh4QJ45fvy4kkGXZu/SnJIKnWin4AEAzm379u1yHEdX1V56wZsxavAevi1btlzytYBCRsED8szxE+2ypZWujmFLKtXf16doNOrqOACA/LZlyxYFA9L0movfYCVjTJmjhnKrrVu3jkAyoHBR8IA8kkqldOpktxy37r9Lc0oGr9/Z2enqOACA/LZ50yZdXp1USdHIXO+qmpi2bd2iZPLSCyNQqCh4QB7p7e2VtVa2OOTqOJkNXLq7u10dBwCQv7q6urT/wAFdPzo2Yte8bkxC/QPhoaMXAFw4Ch6QRzKFy7UdNNMyBZKCBwA4m/Xr10uS3jXm0u+/y7h2dEIBI61bt27ErgkUGgoekEfeKnhlro6TKXgnT550dRwAQP5au3atRpVJEytH7liD8qDVFTVJrV2zesSuCRQaCh6QR06dOiVJctxeohksfdt4AACcLhaLaf26dbphdHTET+15d11MBw8d1rFjx0b2wkCBoOABeaSnp0fSWwXMNSYgU1w6NB4AAKdbt26dorGY3tcQH/Frv69+8JorV64c8WsDhYCCB+SR3t7ewQPOi0pcH8sGyyh4AIAzWrlypapKNCLn371TXcjRZdUprXzl5RG/NlAIKHhAHunt7ZUJlkrG/b+6TlHpYKEEAOA0kUhEa9es1nvroipy6enoxoao9u0/oNbWVncGAHyMggfkkd7eXsnt5ZlpTlGJeih4AIB3WLVqlSLRmD7YOHLHI7zTb42NyxjpV7/6lWtjAH5FwQPyyMDAgFKB4qyMZYtK1NfXn5WxAAD5Y8mSxWooH9zt0i2jSx1dOyqhXy1ZLMdxXBsH8CMKHpBH+vr65GTh/jtpsOANDAxkZSwAQH44fvy4tmzZog+NjYz47pnv9KHGqNo7OrV161Z3BwJ8hoIH5JG+/n7ZbBW8YInC4QFZa7MyHgAg9y1cuFCS9CEXl2dmvLc+ropi6cUXX3R9LMBPKHhAHunvH5Atys4STRUVy0mlFIu5/yQOAMh9iURCC196UTeMiasu5P6yyZIi6SONEa1a9Zq6urpcHw/wCwoekEcikYiUpYJn0/f6RSKRrIwHAMhtr732mk719Oqm8dGsjfl746NKpRwtWrQoa2MC+c7VgmeMqTXGPGeM2WOM2W2M+W1jzGhjzDJjzP70x1FuZgD8wlqrWDQyVLxcHy9dJMPhcFbGAwDkLmut5j/3rBrKra4dPfJn353N2HJH141O6IXnFygeH/lD1QE/cnsG725JS6y1MyS9S9JuSV+XtMJaO13SivTXAM4jFosN3g+XxSWaEgUPACDt2LFDu3bv0e9PGFDA5c1V3un3J0bUdfKUVqxYkd2BgTzlWsEzxlRL+oikOZJkrY1ba09J+pSkuemHzZX0abcyAH4SjQ4uibGBYFbGy8wUcg8eAODpp59SZYn04absPydcOzqhiVWO5j39FBt/AcPg5gzeZZI6JD1ijNlijJltjKmQNNZa2yZJ6Y8NZ/plY8wXjTEbjTEbOzo6XIwJ5IdsFzwFit42LgCgMB05ckRr1qzRTePCKi3K/vjGSJ+YMKDDR5r1+uuvZz8AkGfcLHhBSe+RdL+19t2SBnQByzGttQ9aa2daa2fW19e7lRHIG0MzaYHsPLtmiiQzeABQ2B577DGVBKSPTvDuDb/3j42rLmQ1d+6jzOIB5+FmwWuV1GqtXZf++jkNFr4TxpgmSUp/bHcxA+AbbxW8bM3gDY7DDB4AFK6Wlha9vGKFfm98RNUl3hWrYED65KQB7dmzV+vXr/csB5APXCt41trjklqMMVemv3WTpF2SXpR0S/p7t0h6wa0MgJ8kEoO7ltmszeAVvW1cAEDh+fnPf65gwOoTk7w/MufDTTGNCUlzH32EWTzgHNzeRfNfJT1hjNku6QZJt0m6XdJHjTH7JX00/TWA8xgqWiZLN0CYwX8ekslkdsYDAOSUQ4cOacXy5bppXEQ1Hs7eZQQD0h9N6teu3Xu0du1ar+MAOcvVtV7W2q2SZp7hRze5OS7gR0NFy7j9vozeNg4zeABQmB566CGVBq1unuL97F3Gh5tiWtxaoQd/9oDe//73q6jIg11fgByXpVeKAC7V0BLNLBU8ywweABSs7du3a82aNbp54oCqir2fvcsIBqTPTe3X4SPNWrp0qddxgJxEwQPyRCqVGvzEZOmE2XTBGxoXAFAQrLV64P77VFsmfWxi7m209b76uC6rTunhOQ+xERhwBhQ8IE8M3VCerYIHAChIy5Yt067de/S5Kf2enHt3PsZIfz6tXx2d3Xrqqae8jgPkHAoekCeyv2PYYJF0HCfL4wIAvBIOh/WzB+7X1OqUPtSUu+egXlmb1G81xPTUk0/q+PHjXscBcgoFD8gTbxW8C5vBK21+XaXNr1/4gOad4wIA/O7JJ59UV/dJ/dX0fgVyfMHIn00LS05C999/n9dRgJxCwQPyhBlamnlhhSsQ7lYg3H3hAw6tCM3xZ3gAwIg4cuSInn7qKX2wMabpNbm/wdaYMkefnBTWq6++xuHnwGkoeECeCATSf12zNqNm3z4uAMC3rLX6yY9/rNJASn8+bcDrOMP2ickRNVVY/fQnP1YslrtLSoFs4pUbkCeyXvDs4L13nDEEAP63ZMkSbdu+XX96Wb+qc+BQ8+EqDki3XNGrtuMn9Nhjj3kdB8gJFDwgT7xVtJjBAwCMnO7ubt1/3yxNr0npd8bl3yzY1aOS+mBjVE899aTefPNNr+MAnuOVG5AngsGgJMnY7OxqmRknMy4AwJ/uvvtuhQf69fcz+nJ+Y5Wz+YtpYVUEHd1x+w+UTOb+/YOAmyh4QJ4oLi4e/MTJ0sHj6eMRhsYFAPjOq6++qldffVWfmTKg8RVZen5xQVWJ1d9M79O+/Qc0b948r+MAnqLgAXmipKRk8BObpSfg9DgUPADwp1OnTumnP/mxplQ7+oNJUa/jXLIbG+J6X31Mjz7ysA4fPux1HMAzFDwgT2SKlsnSwePGoeABgF9Za/WTn/xEfX29+ocZvQr65BXh31w5oLJASt//3vdYqomC5ZO/zoD/ZX0GL13whsYFAPjGsmXL9Nprr+mzUwc0qTJ/l2a+U02J1d9e0af9Bw6wqyYKFgUPyBOlpaWSJONk5x3JzDhlZWVZGQ8AkB3t7e26+66fanptUp/wwdLMd3pfQ1wfbIzpscce0+7du72OA2QdBQ/IE0MzaVkqeJmZwkyxBADkP8dx9IPbblMyHtUX83jXzPP56ysGNKrU0fe++x1FIhGv4wBZRcED8sRbM3jZWUqTmcFjiSYA+MczzzyjLVu36q+m9WlseXbu6fZCedDqizN6dOxYm+69916v4wBZRcED8sTQTFrWjklgBg8A/GTfvn2a/dBDmlkf10ea8u9A8wt11aikPjEpokWLFmnVqlVexwGyhoIH5IlAIKCiYDDrBY8ZPADIf5FIRN/9zrdVVZzS383ol/Hp0sx3+uxlYU2pdvTDO25XR0eH13GArKDgAXmkuLhEJku7aBruwQMA35g1a5ZaW4/qizN6VFVsvY6TNcGA9P9e1aN4ZEC33fZ9OVk6agjwEgUPyCMlJSXZ22QlPQ7n4AFAfnvttde0cOFCfWJSRNeMLryz4ZoqHP3V9H5t2bJV8+bN8zoO4DoKHpBHiouDks3SQefpcSh4AJC/2tvbdecP79DU6pQ+e1nY6zie+UhTTO+rj2n27Ie0Z88er+MArqLgAXmkqKhoqHi5ztqhMQEA+SeVSukHt31f8WhY/3x1r4IF/KrPGOnvZwyotsTRd7/zbYXDhVt24X8F/FcdyD9FRUVDxct16SJJwQOA/DR4JMI2/dW0PjX5+EiE4aootvqn9NEJs2bN8joO4BoKHpBHBgte9mbwjDEyhbLVGgD4yN69ezV79kN6X32sII5EGK4Zo5K6eXJYixYt0quvvup1HMAVFDwgjziOlUThAgCcXTQa1fe++x3VFDv6uxkDBXMkwnB9ZmpEU6tT+tGdP1RnZ6fXcYARR8ED8ojjOMraM7UxstbKZmtJKABgRPzsZz9TS/pIhMoCOhJhuIIB6Z+v7lUsMqA77rid5zn4DgUPyCOpVCqLBS/w1pgAgLywYcMGPf/88/r4xIiuLsAjEYarqdzR5y/v14YNG/XQAmJEAAAgAElEQVTCCy94HQcYURQ8II8MFrxs/bU1b40JAMh5fX19uv0Ht2lchdWfFPCRCMN10/iYrhud0P33zVJLS4vXcYARQ8ED8kg0EpENZOdcOls0OE4kEsnKeACAS3PPPffo5MmT+qerelTCBsjnZYz0D1f1qcgmdMftP+ANTfgGBQ/IE9ZaRSLhoeLl+njpcQYGBrIyHgDg4q1Zs0ZLly7VJyeHNbWaojJco0qt/npan3bs3KUFCxZ4HQcYERQ8IE9EIpHBTU+KSrIzYHocCh4A5La+vj79+Ed3amKlo09NYdXFhfpAY1w31MU1+6EH1dra6nUc4JJR8IA80d/fP/hJ1mbwSt4+LgAgJz3wwAM6efKk/mFGr4K8srtgxkh/d2W/imxCP/zhHeyqibzHPwNAnujq6pIkOSXlWRnPKR4chzOCACB3bdu2TYsWLdLvT4ywNPMSjCq1+rPL+rV9+xtavHix13GAS0LBA/JER0eHJMmWVGRlPJsukplxAQC5JR6P60d3/lD15VafmcqumZfqd8bFdGVtUvffN0snT570Og5w0Sh4QJ5ob2+XJNni7BQ8FRXLFJdS8AAgRz311FNqaT2qW6b3qZRdMy9ZwEh/e2W/wuEBzZo1y+s4wEWj4AF5or29XQoEZYOlWRvTKanQiRMnsjYeAGB4jh07pscff0zvb4jp+jEJr+P4xviKlP5wUljLly/X1q1bvY4DXJTzFjxjzFhjzBxjzOL011cbY77gfjQApzt8+LBsqGbwbvAsSZZU6+Chw1kbDwAwPPfec48CNqk/n87SzJH2yckR1YWs7r7rp0omk17HAS7YcGbwHpX0K0nj0l/vk/RltwIBOLM3Dx5SsrQ2q2M6oVFqP3Fc0Wg0q+MCAM5u7dq1WrN2rT49eUCjSx2v4/hOaZH0l9P6dOjwET3//PNexwEu2HAKXp219hlJjiRZa5OS2KYJyKL+/n51dXbIKc9+wbPW6siRI1kdFwBwZvF4XPfe8z8aV2H18Ym8+eaW99QldP2YhB55eI66u7u9jgNckOEUvAFjzBhJVpKMMb8lqcfVVADe5uDBg5KkVGhUVsfNjPfmm29mdVwAwJktWLBAR4+16S+m9XHmnYuMkf5yWr9i0ajmzJnjdRzgggznn4avSHpR0uXGmNWSfi7pX11NBeBtdu7cKUlyKuqzOq4tq5YJlg6NDwDwTldXl34+91G9uy7OxipZ0FTh6KMTIvrlLxdp3759XscBhu28Bc9au1nS70j6gKR/knSNtXa728EAvOWNN96QQjWyxaHsDmyMEhX12rb9jeyOCwD4DbNnz1YsFtWfTxvwOkrB+NSUiKpKpHv+525Za72OAwzLcHbR/GNJfyTpSklXSPqkMeYmY0yD2+EASNZavfHGDiXKszt7l5GqHKvWlmb19vZ6Mj4AQNqzZ4+WLFmsj02IqLGcjVWypaLY6rNT+vXGjp165ZVXvI4DDMtwlmh+QdJsSX+Z/u8hDS7bXG2M+etz/aIx5rAx5g1jzFZjzMb090YbY5YZY/anP2b3piIgzzQ3N6uvr1epqrGejJ8Zd/t2Ju4BwAvWWt17zz2qKpE+PSXidZyC8zvjYppU5eiB++9TLBbzOg5wXsMpeI6kq6y1n7XWflbS1ZJikt4v6dZh/P7vWmtvsNbOTH/9dUkrrLXTJa1Ifw3gLNatWydJSlaP92T8VEW9TFGx1q9f78n4AFDoXn75Ze3YuVOfm9qvUJBlgtkWMNJfTetTe0ennn76aa/jAOc1nII3xVp74rSv2yVdYa3tlnQxd/h+StLc9OdzJX36Iq4BFIy1r78uWz5KtrTSmwCBIsWrmrRm7VruPwCALAuHw3rg/lmaXOXoI03MHnllxqikbmyI6cknHtfx48e9jgOc03AK3ipjzEJjzC3GmFskvSDpNWNMhaRT5/ldK2mpMWaTMeaL6e+Ntda2SVL64xnv5TPGfNEYs9EYs7Gjo2N4/2sAnwmHw9q+bZsSHs3eZaRqJ6qzo4Pz8AAgy5544gl1dHbrb67oU8B4naawfX5aWDaV0H33zfI6CnBOwyl4/yLpEUk3pP9bL8laawestb97nt/9oLX2PZL+QNK/GGM+Mtxg1toHrbUzrbUz6+u92VwC8NqGDRuUSqWUrJnoaY5kzQRJ0urVqz3NAQCFpKWlRfOeflofaoxqek3S6zgFr67M0R9NDuu111Zp48aNXscBzmo4xyRYSW9qcDnmZyTdJGn3cC5urT2W/tgu6XlJN0o6YYxpkqT0x/aLSg4UgFdeeUWmJOTZBisZtqRCTmWDXn6ZHcQAIBustbr77rtUHEjpTy8Pex0Hab8/MaKGcqu7fvoTNlxBzjprwTPGXGGM+YYxZrekeyW1SDLW2t+11t57vgsbYyqMMVWZzyV9TNIODR6afkv6YZklnwDeIRwOa/WaNYrVTJbMcCbb3RUfNUVvvnlALS0tXkcBAN9btmyZNm7cpM9NHVBtKfc/54qSIulvpvep9egxPfnkk17HAc7oXK8a92hwtu6T1toPWWvvkZS6gGuPlfRrY8w2DS7rXGStXSLpdkkfNcbsl/TR9NcA3mHt2rVKxONKjp7qdRRJGsrBOUAA4K5Tp05p1r336PKalG4aH/U6Dt7h+jEJfWBsTE88/rgOHTrkdRzgN5yr4H1W0nFJrxhjHjLG3CRp2Lf3WmsPWmvflf7vGmvt99Pf77LW3mStnZ7+2H1p/xMAf1q+fLlMSbnnyzMzbEmFUlWNWrpsGbtpAoCLZs2apf6+Pn3hSjZWyVV/MX1AZUUp/ejOO5VKXcj8B+C+sxY8a+3z1to/kzRD0kpJ/y5prDHmfmPMx7KUDyhIXV1dWrdunaKjL8+J5ZkZ8THT1NrSop07d3odBQB8adWqVVq2bJlunhzWhEqKQ66qLrH6y8v7tHPXLj377LNexwHeZjibrAxYa5+w1t4saYKkreJwcsBVS5YskeM4StRf4XWUt0mOnipTVKxFixZ5HQUAfOfkyZP68Z0/1OQqR5+aEvE6Ds7jA41xvbc+rjmzH9Kbb77pdRxgyAVNDVhru621P7PW/p5bgYBCZ63VwkW/VKqqUbasxus4b1dUrNioqXr55VcUDrOrGwCMFGut7rzzTg309+mfr+5VMHcWb+AsjJH+7sp+lRel9P3vfVfxeNzrSICkCyx4ANy3ZcsWtR07qnjddK+jnFGi/grFYlEtX77c6ygA4Bsvvvii1qxZo89dNqDxFSzNzBfVJVZ/f2WvDh46rAcffNDrOIAkCh6Qc+bPXyBTHMqZ3TPfyamol60Yo+fmz2ezFQAYAXv37tW99/yPrh+T0Mcnsmtmvnl3XUIfnRDRc889p1WrVnkdB6DgAbmkra1Na9asVrRuuhQIeh3nzIxRtOFqNR85oi1btnidBgDyWl9fn771zW+oujilf7qaXTPz1eenhXVZdUq3/+A2HTt2zOs4KHAUPCCHvPDCC7KSEvUzvI5yTsnRU2WKyzR//nyvowBA3nIcR3fcfrvaT5zQ/7q6R1XFrIrIV8UB6V+u6ZVNRPTNb3xDsVjM60goYBQ8IEeEw2G9+NJLStROli2t9DrOuQWCitZdoTVr1qi1tdXrNACQl+bOnatfr16tP5s2oOk1Sa/j4BLVhxz901W9OnDggH74wx9yGwM8Q8EDcsTChQsVHhhQvPFar6MMS6LhalkT0DPPPON1FADIOytXrtTcuXP14caoPj6B++784t11CX32srBWrFihp556yus4KFAUPCAHJBIJzZv3jFJVjXIqG7yOMyy2pFzx0Zfrl79crO7ubq/jAEDeOHDggH7wg9s0rSalv50xIMN9d77yyckRvb8hpoceelBr1qzxOg4KEAUPyAErVqxQV1enYo3XeR3lgsSbrlMymdCCBQu8jgIAeaGjo0P/9fVbVW7i+t/X9qiYV2K+Y4z0D1f1a3KVo+9+59vav3+/15FQYPhnBfBYKpXS4088IVs+WqmaCV7HuSC2rEaJUZM1f8EC9ff3ex0HAHJaOBzWf339VvWd6tZXrjul2lLu0fKr0iLpy9f1KKSYvn7r19Te3u51JBQQCh7gsZUrV6q1pUXRpncpH9fpxJtuUCQcZhYPAM4hmUzq29/6lg4ePKh/uaZHk6s4zNzvRpc6+sr1pxTuPamv3/o1DQwMeB0JBYKCB3jIcRw9OneubPkoJUdN8TrORXEqxihZO1Hz5j3DkxcAnIG1VnfddZfWrV+vW67o17vGJLyOhCyZVJnSl67p1eHDh/XNb/wfJZPslgr3UfAAD7366qtqaW5WtDE/Z+8yYuPerYGBfmbxAOAMHnvsMS1cuFA3T47od8dzPlqhuW5MQn9/Zb82btrM8QnICgoe4JFUKqWHH3lECtUqOXqK13EuiVNRp2TNRD319NPq6+vzOg4A5IzFixfr4Ycf1gcbY/qTy8Jex4FHPjIupj+eGtbSpUs1Z84cr+PA5yh4gEeWL1+uluZmRca9WzL5/1cxNv49Cg8MaN68eV5HAYCcsG7dOt155526dnRCX5jRn88LNTACPjUlot8dF9Xjjz+uX/ziF17HgY/l/6tKIA8lEgnNmfOwbEVd3t57905OxRglRk3Vs88+x7l4AAre3r179c1v/B9NrEjqX6/tVZBXXAXPGOlvrhjQu+viuvvuu7Rq1SqvI8Gn+OcG8MDChQvV3n5CkfHvyet7794pNuE9isVjeuKJJ7yOAgCeaWtr09dv/ZoqAnF99fpTCgW9ToRcURSQ/tc1fZpaldJ3v/Md7dy50+tI8CEKHpBl4XBYjzw6V05Vo1LV472OM6JsWY3iY6brFy+8oLa2Nq/jAEDW9fT06Gv/+R+KDfToP67nrDtJenxfuY70FelIX5Fu21ytx/eVex3JU6VF0leu71FtcVz/9fVb1dLS4nUk+AwFD8iyefPmqbfnlCITZvpq9i4jPv7dcqw0e/Zsr6MAQFYlEgn9n///v9V27Kj+/boeja/grDtJau4PKpIKKJIKaM+pYjX3M6VZXWL1n9efko3169av/ad6enq8jgQfoeABWdTV1aWnnn5aiVFT5FQ2eB3HFbakQtGGa7RixQrt2bPH6zgAkBXWWv34xz/W9jd26B9n9OnKWs47w7mNLXf05et61H7iuL75jW8okeB8RIwMCh6QRY888ojiiYRiE2Z6HcVV8abrZIrLdP/993PeD4CCMG/ePC1ZskSfnhLWbzfGvY6DPDG9JqkvzOjT1m3bdNddd/GciRFBwQOy5ODBg1q0aJHidTNky6q9juOuohJFmm7Qtm3btHbtWq/TAICr1q5dq5/97AHd2BDTp6dGvI6DPPPBxrg+OTmsRYsWacGCBV7HgQ9Q8IAsue/++6WiEsXG3+B1lKxI1M+QQrW6d9Z9SiZZqgTAn9ra2vT9731Xkyod/eNV/Qr479ZqZMFnL4voPXVx3XffLHbWxCWj4AFZsG7dOm3csEGRpndJwTKv42RHIKDwhJk6drRVL774otdpAGDEJRIJfftb31QqHtaXrulRaZHXiZCvAkb6x6v6Nbo0pW998xtsuoJLQsEDXJZMJnXvrFlSqEaJhqu8jpNVqZqJSlWP05yHH1FfX5/XcQBgRD3wwAPas3ef/uHKPo0td7yOgzxXUWz1pat7dLK7S7fd9n05Dn+mcHEoeIDLFi5cqJbmZkXGv1cKFNjbu8YoOvFGDQz0a+7cuV6nAYARs2HDBs2fP18fmxDR+xrYVAUjY2p1Sn8xrV/r1q3XCy+84HUc5CkKHuCivr4+zZ4zR6nqJiVrJ3sdxxNO+WjF667QggXPc5grAF8Ih8P60Z0/VFOF1Z9eHvY6DnzmpvExXTs6oQd/9oBOnDjhdRzkIQoe4KLHHntM/X19ik680ZeHmg9XfPx7ZANFuu+++7yOAgCXbM6cOTrR3qG/v7JXJQW2MAPuM0b6uyv75STj+smPf8zRCbhgFDzAJa2trZo/f77idVfIKR/jdRxP2eKQoo3Xa+3atdq4caPXcQDgou3Zs0cLFszXTeOjHGYO19SHHH1uar/WrV+vl19+2es4yDMUPMAl999/vxwFFJ/wHq+j5IT42Guksmrde+8spVIpr+MAwEV56MEHVVkslmbCdR+dENXkKkdzZj/EcUO4IBQ8wAVbt27V6tWrFW28Xra43Os4uSFQpMj49+rw4UNavHix12kA4IJt2bJFmzZv1s2TBhQKsmwO7goY6Y+n9utY23EtWbLE6zjIIxQ8YIQ5jjN4LEJppeKN13gdJ6ckR02RU9Wg2bPnKBzm3W8A+cNaq4fnzNaoMumm8VGv46BA3DAmoctrUpr76COKx9mtFcNDwQNG2PLly3Vg/35Fxr1HCgS9jpNbjFFkwo06deqknn76aa/TAMCw7dmzR2/s2KmbJw2wsQqyxhjpM1MG1NHZpZUrV3odB3mCggeMoHg8rodmz5atqFNyzOVex8lJTmWDEqOm6umn56mrq8vrOAAwLIsXL1ZJkfShxpjXUVBgrh2dUH251eLFv/Q6CvIEBQ8YQS+99JI62tsHDzUv4GMRzic24b2KJxJ67LHHvI4CAOcVi8W0YvkyzayLce8dsi5gpA+PjWjLlq06fvy413GQByh4wAgJh8OaO/fnSlU3KVU9zus4Oc2WVSted4VeeuklHTt2zOs4AHBOa9eu1UA4og83ce8dvPHB9MzxsmXLPE6CfEDBA0bIs88+q97eHkXHz2T2bhji426QI6OHH37Y6ygAcE6bNm1SqFi6ahRb1cMb9SFHk6scbd60yesoyAMUPGAE9Pf36+l585QcNUlOZb3XcfKCLSlXtP4qrVixQs3NzV7HAYCz2rZ1i66ojivAe3fw0JU1Me3cuZPdNHFeFDxgBPziF79QJBxWrOkGr6PklUTjtVKgSE8++aTXUQDgjLq7u9Xc0qoZtQmvo6DAzRiVVDyR0N69e72OghxHwQMuUSQSGZy9q5kgp6LO6zh5xRaHFKu7UkuXLlVbW5vXcQDgNxw4cECSdHk1yzPhrcurB99k2L9/v8dJkOsoeMAleumll9Tf16fYOGbvLka88Vo5MsziAchJmV0L60OOx0lQ6GpLrIqLxE6aOC8KHnAJksmknnn2WaWqGuVUNngdJy/ZkgrFx0zTkiVL1NPT43UcAHibtrY2FQWkUaUUPHjLGKk+ZCl4OC8KHnAJ1qxZo86ODsXHXu11lLyWaLhaiURCixYt8joKALxNe3u7xpSJDVaQE8aUJHS8jeOFcG6uFzxjTJExZosxZmH666nGmHXGmP3GmHnGmBK3MwBumT9/vlRaqWTtJK+j5DWnfJRSVU1a8PzzSia5zwVA7ohEIgoVMXuH3BAKWkUiYa9jIMdlYwbv3yTtPu3rOyT91Fo7XdJJSV/IQgZgxB08eFDbtm1TrH6GZJgMv1TxhqvU2dGhtWvXeh0FAIZEo1EVByh4yA0lAatYLOZ1DOQ4V1+VGmMmSPpDSbPTXxtJvyfpufRD5kr6tJsZALcsWbJECgSUqLvC6yi+kBw1SSqt0C9/+UuvowDAkFgsqpKA9ToGIEkqKZLiMc7Bw7m5Pe1wl6SvScq89TVG0ilrbWYNVquk8Wf6RWPMF40xG40xGzs6OlyOCVyYVCqlpcuWKVE9Qba4zOs4/mACio+aqnXr1+vUqVNepwEASZJ1rIwoeMgNRpJj+fOIc3Ot4BljbpbUbq3ddPq3z/DQM/4ptdY+aK2daa2dWV9f70pG4GJt3rxZp06eVHLMNK+j+EpizDQ5qZReeeUVr6MAgCSpuKREScsOK8gNSUcqLg56HQM5zs0ZvA9K+iNjzGFJT2twaeZdkmqNMZk/mRMksRUQ8s7SpUtlgqVK1k7wOoqvOOWjZctH61dLl3odBQAkScXFxRQ85IykNSoOFnsdAznOtYJnrf0va+0Ea+0USZ+X9LK19i8lvSLpc+mH3SLpBbcyAG5IJpP69erVitdOkgK8izbS4qOmas/u3ers7PQ6CgAMFjyHjbSQGxKOVFxCwcO5efEv1q2SvmKMOaDBe/LmeJABuGhvvPGGIuGwkrUTvY7iS5kjJ9hNE0AuCIVCilLwkCOiKaNQqNzrGMhxWfkXy1q70lp7c/rzg9baG62106y1f2KtZa9X5JU1a9ZIgSIlq8+4PxAukROqlcqqtHr1Gq+jAIDKy8sVTbJEE7khljQqr6j0OgZyHG9JARfo16vXKFnVKBWxRMIVxihePVGbNm1UNBr1Og2AAjdY8Ni1ELkh6hSpvJwZPJwbBQ+4AG1tbWo7dlTJGjZXcVOydoISiYR27NjhdRQABS4UCimWkhw6HnJA1AkoFAp5HQM5joIHXICtW7dKklLV4zxO4m+pyrGSCWjz5s1eRwFQ4DIvpuMpj4MAkmIpQ8HDeVHwgAuwefNmmZKQnLJar6P4W1GxUpX12rSJggfAW2VlZZKkmMN9ePBeLPnWn0ngbCh4wDBZa7Vx0ybFKxslwxO925JVTdq3b6/6+/u9jgKggJWWlkqS4in+3Yf34ik79GcSOBsKHjBMR48e1cnubqWqmryOUhBSVU2y1nIfHgBPBQKDL5W4BQ+5wNFbfyaBs+FPCDBM27ZtkySlqho9TlIYUhX1UiAw9P87AHjBpFdsWBoecoC1FDycH39CgGHatm1b+v67Gq+jFIaioJzyOm1Jb2wDAF7IFDzH4xwAbzJguCh4wDBYa7Vl61bFK8bm1f13pc2vqyjcpaJwl0J7fqnS5te9jnRBEpWN2rdvnyKRiNdRABSoWCwmSSrhFRM8ZoxUHJDi8bjXUZDj+OcKGIajR4+qo70975ZnBsLdMqmETCqhYN9xBcLdXke6IKnqJjmplLZv3+51FAAFKvMGU1kR0yfwXlmxUTgc9joGchwFDxiGTZs2SZKSNeM9TlJYUlVjpUCRNmzY4HUUAAUq82KagodcUFZkKXg4LwoeMAwbNmyQyqpkS6u9jlJYAkGlKsdq3fr1XicBUKB6enoUKjYq4hUTckBlMKVTp055HQM5jn+ugPNIJpPatGmz4lXj8ur+O79IVI9XS3Oz2tvbvY4CoAAdP35cdWUpr2MAkqS60qROtB3zOgZyHAUPOI/169crEgkrWTvR6ygFKVk7SZK0cuVKb4MAKEjHjx1VXWnS6xiAJGlMmaPjJ9pl2VIT50DBA85j+fLlMsVlSlVP8DpKQbKhGjkVdVq6bJnXUQAUGMdx1MYM3iWLJI3Kysr0uc99TmVlZYokWQ1zsepDjuKJhDo7O72OghxGwQPOIRwO69e/Xq1Y7RSJg0U9Ex99mQ7s36/m5mavowAoIEePHlUkGtOkSgrepQgnjW6++WZ96Utf0h/+4R8qTMG7aJMqB2eT9+/f73ES5DJesQLn8Nprrykejyk55jKvoxS05OjB//+XLl3qcRIAhWTPnj2SpMuqWaJ5KcqDVgsXLtQ999yjRYsWqTzI8sKLNbkyKWOkvXv3eh0FOYyCB5yF4zh64sknZctHKVU51us4Bc2WlCtZO0kLnn9e/f39XscBUCD27NmjkiJpXDkzeJciFLSKRqOaP3++otGoQhS8i1YWlMZVOENvPgBnQsEDzuLVV19VS3Ozoo3vYvfMHBAbd4PCAwN6/vnnvY4CoEBs2bxJl1cnOSIBOeWK6rje2L5NySQzyzgz/skCzsBxHD06d65sqFbJ0VO8jgNJTkWdkjUT9fS8ZzjkFYDrOjo6dPDQYV0/OuZ1FOBtrhuTUDgS1Y4dO7yOghxFwQPO4OWXX9aRw4cVbXqXZPhrkiti427QQH+fnnnmGa+jAPC5DRs2SJKuH5PwOAnwdteMSqjIDB7jBJwJr1yBd+js7NRP77pbTmW9kqOneh0Hp3Eq65UYPVU/f+wxdhAD4KrVq1drVJk0oYL775BbQkGrK2qTWv3rVZyHhzOi4AGnsdbq9jvuUDgSUXjKR5i9y0HRyR+QLSrVd777XcViLJ0CMPL6+vq0ft3rurE+wi3YyEk31kd1pLlFBw8e9DoKchCvXoHTvPDCC9q4YYMi42fKhmq8joMzCZZqYMqH1NLcrIceesjrNAB86LXXXlMimdJvj+VNJOSmGxviKjLS8uXLvY6CHETBA9L27NmjWffdp1TNeCUarvI6Ds4hVTNB8Yar9Nxzz2nVqlVexwHgM8uXLVNjhdXUKpZnIjdVlVhdOzqu5cuWynEcr+Mgx1DwAEkHDx7UV//jP5UIlCoy5cMci5AHYhPeJ6eyQd/69reHNkMAgEvV2tqqLVu36oMNLM9EbvtgY0wdnV08B+I3UPBQ8FpbW/XvX/mKwglH/dM/LltS7nUkDEdRUAPTP6pkaY3+v//+b23fvt3rRAB8YOHChQoY6SPjol5HAc5pZn1c1aXSSy+96HUU5BgKHgra8ePH9W9f/nf1DkQHy11ZtdeRcCGCpRqY/jElisr1tVtv1Z49e7xOBCCPxeNxLf7lIr17TFyjStmdELktGJA+PDaiNWvWqrOz0+s4yCEUPBSsgwcP6t/+7cvqPtWr/ukflxOq9ToSLoItDqn/io8raoP66lf/Q5s3b/6/7d13fNX1of/x1+eck3GyNxAChB2GMgVEFEGmxqK3dbVqLVZ7bf21t9ppr61tr9RaV92i4qgDV5WlRTaCIDJkhT0FAgkJ2cmZn98fiRTvdaAm+SYn7+fjwQPyzRlvMJ7zfZ/P5/v5OB1JRFqp5cuXU15RyZiOGr2T1uH87DpsOMycOXOcjiItiAqetElLly7lppt+zLETFVT1nEA4Pt3pSPIN2Oh4qnpNospGceutt/Lqq69qbyAR+creeP012sdb+qdpc3NpHdrFhTkz3c/sWW/h9/udjiMthAqetCmhUIgnnniCO+64g9qoJKr6fItwQqbTsaQR2JhEqvLyCaR05tFHH+XPf/4zdXX6FF5ETk9BQQHbtu9gQsdqXFpcRVqRCTl1nMUeAGcAACAASURBVCgrZ+nSpU5HkRZCBU/ajIqKCn7161/z8ssv48/Mo7r3ZC2oEmncUdR2H4uv4xAWL17MTT/+MYWFhU6nEpFW4I033sAbVb8yoUhr0j8tQHa85Y3XX9PsFQFU8KSNWLVqFVOvv5516zdQl3sOvtyR4HI7HUuagjH4swdQ03M8+w8e5vof/pC3335bb3oi8rmOHz/O0qVLOK99LV6P02lEvhpjYFzHanbs3EVBQYHTcaQFUMGTiFZSUsIf/vAHfvvb31JcFaC694UEMns7HUuaQSilE5V9LqbKlcjdd9/Nz372Xxw8eNDpWCLSAs2aNYtwKMz4HE3rltZpVHsfcVHw+uuvOx1FWgAVPIlI4XCY2bNnc/U117DsvRX4Og6mqu8UXW/XxtjYJKp7T6Yu9xw2F2znB1On8txzz+lCdBE5yefzMWfWWwzM8JPlDTsdR+RrifXA6Pa1LFu2jKKiIqfjiMNU8CTi7Nu3j//3/37KfffdR5Unmap+l+DPHqgpmW2VMQQye1PZ/1LqkjrxzDPPMPX667UxuogAsGzZMsoqKjV6J63euJz6LRPmzZvndBRxmAqeRIzi4mLuv/9+rr/+hxTs3E1t13Op6TUJG5vsdDRpAWxUHHXdx1DTczyHisv46U9/yh133MGBAwecjiYiDpr11lu0j7f0S9XWCNK6ZXrDnJkeYO7sWQSDQafjiIN0KbG0emVlZbz00ku8+eabBIIh/Bm98HcchI3yOh1NWqBQSicqE9sTXbiJZe+tZNmyZUyYMIHvf//7ZGdnOx1PRJrRnj172FpQwFU9ajDaGkEiwNiOtdy/qYz333+f8847z+k44hAVPGm1KisreeWVV3jttdfx+X0E0rrj6zgIG5PodDRp6dxR+HOGEGjXl+jCTcxfsJAFCxeSf9FFXH311WRlZTmdUESawbx584hywagO2hpBIsOA9ADpXpgzZ7YKXhumgietTk1NDW+88QYvz5xJTXU1gdSu+HsOIuxNcTqatDI2youv83D87fsTfWQjs+fO5e233+HSSy/hu9/9LqmpqU5HFJEmEgwGWbxwAYMyfCRGaRsViQwuA+dk1TB37TpKSkpIT093OpI4QAVPWo3i4mLefPNNZs2aTXV1FcGUTvj6jSMcpxcv+WZsdDy+3JH4O5xBzJGPeO3113lr1iwmT5rEd77zHTp37ux0RBFpZGvXrqWsopKzu2j0TiLL2e39zD4Qx5IlS/jOd77jdBxxgAqetHjbt2/ntddeY8mSpYRtmEBKZ/ydzyecoGl00rhsTCJ1Xc/F1/5Moo9uYs7cecyePZthw4dz+WWXMWTIEIwu1BGJCAsXLiQ+qn5Km0gk6RgfoktimAXvzlfBa6NU8KRFCoVCvP/++8x85RW2btmC8UTjy8zDn9UHG5vkdDyJcNabjK/rufhzhhJVtJ0PN2xizQcfkJvblcsvv4wLLriAmJgYp2OKyNcUDAZZvep9BmfU4dF64hKBhmXW8trOXRw/fpyMjAyn40gzU8GTFqWmpoa3336b115/nWNHj0JsInWdhhHI6AWeaKfjSRtjo7z4Ow7C3+EMPCV72VdUwN13383jT0zn0kumMGXKFNLS0pyOKSJf0fbt26mqrmFAV43eSWQakB7gtb3w4YcfMnnyZKfjSDNrsoJnjIkFlgMxDc/zurX2D8aYrsBMIA1YD1xjrfU3VQ5p+ay1bNu2jXnz5rFw0SJ8dXWEE9vh6z6WYGpnMPp4VRzm8hDM7EVVRk/clYUEj27lueee44UXXmDUqFHk5+czZMgQXC79rIq0BmvWrMEYtPedRKxOCSFSYup/1lXw2p6mHMHzAWOttVXGmChghTHmHeAW4H5r7UxjzOPA9cBjTZhDWqjy8nIWLFjAnLlzObB/P8btwZfalUDXPMIJmU7HE/m/jCGUlE1tUjamtpzo4u0sf/8Dli1bRmZWFvkXXcTkyZO1zYJIC7d+3Tq6JQWJ1+qZEqHqP8CoY8O6tVhrdf14G9NkBc9aa4Gqhi+jGn5ZYCzw3YbjzwF3oILXZoTDYTZs2MC8efNYtnw5oWCQcEIm/txzCKR1BbemYUrrYL3J+DoPx5czFE/ZAY4W7+SZZ57h2Wef5ayzziI/P5+RI0fi8WgmvEhLEg6H2bNnN+dkaPROIlvXxBArj1ZSUlKi6/DamCY98zDGuIF1QA/gEWAPUGatDTbc5BDQ8XPueyNwI6AlyiNAcXEx//rXv5gzdx5Fx45iomLwpfUikNmLcJyuYZJWzOUmmNaNYFo3jK+SqOO7WLNxK2vWrCEpOYULJ0/iwgsv1OuYSAtx5MgRaut8dEkIOR1FpEl1Tqg/3d6zZ48KXhvTpAXPWhsCBhpjUoA3gT6fdbPPue90YDrA0KFDNYeiFaqurmb58uW8u2ABH23YgLWWUFIH/N1GE0ztAi6NbEhksTGJ+DsOxp89EHf5YQLFO5n5yqvMnDmT3r3zmDBhPGPHjtUG6iIO2rt3LwCdEoJfckuR1q1Tw4cYu3fvZvjw4Q6nkebULGfY1toyY8xSYASQYozxNIzi5QBHmiODNI9AIMCaNWtYsGABK1euJBAIQGwSvg4DCKT30BYH0jYYF6GUToRSOuEL1OA5vofth/ay46GHeOSRRxg6dCjjx49n1KhReL1ep9OKtCnHjx8HIDM27HASkaYVH2WJjzInf+al7WjKVTQzgUBDufMC44C/AkuA71C/kub3gVlNlUGah7WWLVu2sGDBAhYtXkJ1VSUmKhZfag8C6d0Jx2fWX+0r0gbZqDgCHc4g0OEMXLUn8JTsYc3GAtasWUNMTCznnjuK8ePHM2TIEF2vJ9IMysvLAbTAirQJCdGWyspKp2NIM2vKs4kOwHMN1+G5gFettXONMQXATGPM/wAbgKebMIM0oQMHDrBgwQLeXbCw/ro6twd/cicCPUcQSuoIWjJe5FPC3lT8OUPxdxyCu+oY/pI9LFr6HgsXLiQpOYXx4y5g3Lhx5OXlacUzkSZSWVlJfJTBpf/FpA2Id4dOfqghbUdTrqK5CRj0Gcf3AsOa6nmlaR0+fJglS5awcNFi9u/bC8YQTMwm0PVcgqm54I5yOqJIy2cMocT2hBLb4+s8Ak/5IQIle3jjzbd44403aNe+PReMHcuYMWPo0aOHyp5II6qrqyPGo9E7aRtiXGHq6mqdjiHNTPOB5EsdPXqUpUuXsnDRInbv2gVAOCELf6fhBNO6YqPjHE4o0oq53ARTuxBM7UJd0IfnxAEOl+7jpZdf5qWXXqJjTg7jLriAMWPGkJub63RakVbP6/VSF9SHJtI21IVdtItPcDqGNDMVPPlMxcXFLF26lEWLF7N92zYAwvEZ+HPOqi91MXqxEGl0nhiCmb0IZvbCBGrxnDjAwRP7eO6553juuefokpvLBWPHMnbsWHJycpxOK9IqxcfHUxuwhC2apikRrzbkJi5OH8S3NSp4clJpaSnLly9n0eLFbNm8GWstNj4df8chBNK6agVMkWZko7wEsvIIZOVhAjV4Svezr2QfM2bMYMaMGXTv3oMLLqifxtmhQwen44q0GnFxcVigNmi00IpEvOqAIT4+3ukY0sxU8Nq4srIyli9fzuIlS9j40Uf1pS4uFX+HgQTTuhL2pjgdUaTNs1FxBNr1JdCuL8ZXhefEfnYd28ee6dOZPn06vXvnMXbsGM4//3zatWvndFyRFq1z584AHKlx0zNZe+FJ5KrwGyr9VjM+2iAVvDaovLyc9957j8VLlvDRhg2Ew2HwJuPrMIBgai7huDSnI4rI57AxCQTa9yfQvj/GV0lU6T62HdrPjsce47HHHiOvTx8uGDuW0aNHk5WV5XRckRanZ8+eAOyv9KjgSUTbX1l/mt+rVy+Hk0hzU8FrIyorK1mxYgWLFi9m/br1hMOh+g3I2/VvGKlL0151Iq2MjUnE3+FM/B3OxNRV1Je9g/vZ/sgjPPLII/Tr15+xY8cwevRoMjIynI4r0iJkZGSQkpTI/so6p6OINKkDlW4AevTo4XASaW4qeBGsqqqKFStWsGTJEtauXUso1FDqsvoSTOtWP1KnUicSEWxsEv7sAfizB2Dqyokq3ceWffvZ+tBDPPzww/Tr358Lxo7lvPPOIz093em4Io4xxtDvjDPZum4lYVuthVYkYm05EUPnTjkkJiY6HUWamQpehPH5fKxevZqFCxeyatVqgsEAxCbiz+xLIK0r4bh0lTqRCGdjk/FnD8SfPRBXbRme0n1s3rOfLZv/zoMPPsigwYMZP24c5557LgkJWhFX2p4xY8awcuVKdpV76J2iaZpNrXNC8ORoUpfEEJ0T9G/e1E74DNtPeLj2Wxc4HUUcoIIXAYLBIBs2bGDhwoUsW76cutpaTHQcvvSeBNK6EY7PVKkTaaPC3hT8HQfh7zgIV+0JPCV7Wb91F+vXrePee+/j7LNHMG7cOEaMGEFMTIzTcUWaxciRI4mJjuKDYzEqeM3g6l41HKyqP+W8bXCFw2nahjVFMVhg7NixTkcRB6jgtVLWWgoKCli0aBELFy2morwM44nBl9KZYKduhJI6gHE5HVOcFvITGxtLfn4+c+fOpSrkdzqROCjsTcWfMwR/x8G4qouJKt3Lig/W8d577xHr9TL6vPMYN24cgwYNwuPR24NErri4OEacPZLVq5ZxefdqYvXjLhEkbGF5oZfu3brSpUsXp+OIA/SS1srs37+fhQsX8u6ChRQdOwouN4HkTgR7DCaYnAMu/SeVfzNBP/nfyufmm2/GWsurc+Y7HUlaAmMIJ2ThS8jC12kY7opCAqV7eXfREubPn09ScgoXjB3DuHHj6Nu3L0YzACQCXX755SxbtoyFh2PJ76IFVyRyrCuO5uMqF7f99Cqno4hD1AZagZqaGpYuXcqcuXPZVlAAxhBMyibQ9VyCqV3AHe10RGmhrCeauXPnYq1l3rx5WE+c05GkpTEuQskdCSV3pC58Np7yQwRK9vLmrNm8+eabdOrchW9dnM/48eNJSdG+mBI5+vXrx/Bhw3j7ozVc0LEOr86IJAKELby1P56cjtmantmG6eWshbLWsn37dubNm8eChQvx1dVhvSn4Og0jmN4dG+V1OqK0Bu5o6mpKeeONN+q/TtQJunwBl4dgai7B1FzqQn6iSvdx4PguHnnkER5/4gnOHTWK/Px8Bg8ejMulKeDS+v1g6lT+8z/XMP9jL5d0rXU6jsg3tqaofvTuv/9rqqbat2H6L9/CVFRUsGDBAubMmcv+/fsw7ij8qbn4c3sRTsjSYiki0jzc0QQyexPI7I2rppSo4ztZtnI1S5cuJSurHfn5FzFp0iRtpi6tWl5eHqPPO4/ZK5YzNNNPTkLI6UgiX1uF3/DC7kR69OjOmDFjnI4jDlLBawGstWzcuJE5c+awbNlygsEA4fgM/F1GEkjrBh5NwRQR54Tj0vB1HoEvZyieEwcpPL6TGTNm8MwzzzBs+HAuzs/n7LPPxu12Ox1V5Cv7r5//nI8+2sD07WF+P/gEHg1OSytkLTy3I57akJvbbvudXo/bOBU8B1lrWbVqFc//4x9s37atfhXMtB4EMnvV71cnItKSuDwE07sRTO+Gqasg6vgu1mzYzAerV5PTqRPXXnMNY8eO1bQgaVVSU1O59Re/5Pe//z2z93v5j26aqimtz+pj0XxYHMONN15Pt27dnI4jDtO7sAPC4TDLly/n+X/8g7179kBsInVdRhLI6KFVMEWkVbCxSQ1bLgzCc2I/HxduYtq0aTz19Ayuufp7TJw4kehozT6Q1uG8hi1CZi1aSPekIAMyAk5HEjltByvdPLszib5987jiiiucjiMtgNpEMwoGgyxevJh//OMFPv74IHiTqe16LsG07qAFC0SkNTIugmndqErtirv8Y44WbuTee+/lmWef5btXXUV+fj6xsbFOpxT5Urfccgv79+/jkYK9/G7QCbok6no8aflKfS7u3ZxCfHIqf/zjnzQ1UwBQq2gGoVCIuXPn8r2rr2batGkcLKmittv5VPa7lGBGT5U7EWn9jCGU0pnqvHxqek2k2B/Fww8/zOWXX8GLL76Iz+dzOqHIF4qLi+Ouu/5KYkoa921OobRO783SstUG4f5NydQRw11/vZvMzEynI0kLoVevJlZcXMwtt9zCPffcQ2FlkJoe46jqO4Vgejcw+ucXkQhjDKHkjtTkXUhN3oWcMAk8+eST3HDjjezbt8/pdCJfKCMjg7/e/Td8JpZ7NiVT4dfK1dIy+ULw0JYkPq72cMcf/0SPHj2cjiQtiBpGE3r//ff5wdSpbNqyldrcUVT3uZhQamdtdSAibUIosT01vSZS02sCHxcWceONNzJ79mystU5HE/lc3bp143/unEZxIJY7N6RSopE8aWFqg4Z7Niaz9UQUv/zlLxk+fLjTkaSF0atWE/D7/Tz00EPcdtttVIajqewzhWBmLxU7EWmTQsk5VPadQp03k/vuu48//OEPVFZWOh1L5HMNHjyYv/3tHsrDXu7ckMqxGp0uSctQGTDc9VEyuyujuf323zN58mSnI0kLpFesRnbo0CH+86abeOONN/Bn9aUq7yKsN9npWCIijrJRcdT0mkhdzlksf28FP5g6lYKCAqdjiXyuM888kwf+/iB+TwJ3bkjlYJUWrxBnlfpcTNuQwqHaGP7nf+5k7NixTkeSFkoFrxFZa/njn/7EvoOHqOk5Dl+XEdr2QETkE8YQ6HAG1XkXcbyyjt/89jbq6uqcTiXyuXr16sWDDz2MOz6VP69PZU2Rtv4QZ+wo8/CHtamUBr3cffffOPvss52OJC2YCl4j+vDDD9m1cye1HYcSSunsdBwRkRYpnJBJTe65VJSX8fbbbzsdR+QL5ebm8sT0J+neK4+HtyTy6p44wrqMVJqJtbDwUAx3bUgmIb0Djz72OIMGDXI6lrRwKniN6Lnnn4eYBALpWslIROSLhBLbE05sz4svvkQgoE2lpWXLyMjggQf+zsUXX8zcA17u2ZhEVUDX1UvT8ofgqe3xPL8zgbOGD+fxJ6bTtWtXp2NJK6CC10g2btzI1i1bqGvXH1yapy8i8mXqOpxJSclx5s+f73QUkS8VHR3Nrbfeyi9+8Qt2VMTy+7VpbD+hyzCkaRyqcvPn9am8VxjLtddey7RpfyExMdHpWNJKqOA1ki1btgAQSurgcBIRkdYhlNAOXO6Tr58irUF+fj4PPvQwMakd+MuGZF7eHYc/5HQqiRRhC+8cjOUPa1MoN8lMmzaNqVOn4nLplF1On35aGsnEiROJjo4h+shHTkcREWkVoo9uhnCIKVOmOB1F5Cvp06cPTz09g4u/9S3eOejljnWpHKzU7B35Zo7XuvjrR8m8vDues0aM5JnnnmfkyJFOx5JWSAWvkWRkZHDllVcQVboPV1Wx03FERFo0468h9thWxowZQ58+fZyOI/KVeb1ebrnlFu666y5qotL4w7oUZu/3Egw7nUxam7CFZUdi+N3aVPbXxvOrX/2KO++8k9TUVKejSSulgteIrrzySpKSU4g99CGE9QovIvKZrCXm0FpchLnhhhucTiPyjYwYMYIZzzzLqHNH8/reOG5fm8qOMl2bJ6fnUJWbv2xI5untCfTM68/TM57hwgsvxBgt4iNfnwpeI4qLi+PGG36Iu/IoCdtm4a4odDqSiEiL4qopJX7H20SV7Oayyy4jOzvb6Ugi31hKSgp//OMfmTZtGgFvFneuT+bJbfFU+nWSLp/NF4JX98Rx+4cpHAkm88tf/pIH/v6gXhOlUegjpkaWn59PWloaDzzwd4p2vEMgvTu+Tmdho+KcjiYi4pxQgJjD64kuKiAhIYGbfvlLJk+e7HQqkUY1cuRIBg0axPPPP8+rr77ChpJYruxWxagOPlzqetJg4/Eont+dSHGNYeLEidx0002kpKQ4HUsiiApeExg5ciRDhgzhxRdf5MWXXiK6/GNqswcTyMoDo0FTaT7huDRsTQkAobh0wnFpDieSNsdaPCf24T30IfhryL/oIm644QaSk5OdTibSJLxeLz/60Y8YP348995zD08VFLCk0Mt3e1TRMznodDxx0JFqFy/vTmBjSRSdO+Vw/523atNyaRLGWut0hi81dOhQu3btWqdjfC2HDh3i/gceYN3atdj4dGqzBxNK7qiiJ83Gu/1tAGrzLnQ4ibQp1uKqKiL2yAbcFUfo3r0Ht956C3379nU6mUizCYfDzJ8/n6emP0HJiTJGZPm4vHsNGd62d53+tPVJANw2uMLhJM2vMmB4a5+XRYe9xMbGcs213+fb3/420dHRTkeTVsYYs85aO/TLbqcRvCaWk5PDPX/7G8uWLePBhx6idNcCTHQcvrTuBNJ7EI7TCkkiEjmMr5Kokj3ElO6G2gq8cXHc8NOfMmXKFNxuLSMvbYvL5WLy5MmMHj2amTNnMvPll1m3JoZJOTXkd6nFq7OwiBYMw8LDsczaH09t0HBRfj5Tp07V6pjS5PTS0gyMMZx//vmMHDmS1atX869//YvVH3xA9NHN2PgMfGndCaZ3w0Z5nY4qIvLVhfx4SvcTXbIbd+VRAAYMGMCkSZMYPXo0cXG6Blnatri4OKZOncpFF13E9OnTmbNoEe8djeOS3CrO6+DDo0k9EcVaWFsczWv7EjhabRg6ZDA//snNdOvWzelo0kZoiqZDysrKWLRoEe/861/s3rULjItgckcC6T0IpnQGlz7plsahKZrSJGwYd0UhUcd3E11+ABsK0iG7I5MnTWTChAm0b9/e6YQiLVZBQQGPPPwwWwsKaB9v+U7XKs7K9BPJK+O3lSma2054eGVPAnsr3HTulMNNP/4JI0aM0LYH0ihOd4qmCl4LsHfvXhYsWMC/5s/nRGkpJioGX0pXgqmdCSW2B5cGWuXrU8GTRhMO464uwlP2MdEn9oKvmrj4eMZdcAETJ06kb9++OokROU3WWlauXMmT05/gwMGP6ZYU4vJuVfRNi8yFWCK94B2odPPa3ng2lUSRkZ7GD6Zez8SJE/F4dA4njUcFrxUKhUKsX7+e+fPns2z5cgJ+P7jchBLaEUjuSCipI2FvKhH9EZ80OhU8+SZMXQWeisO4yw8TXXUUG/TjcrkYPnw4EydO5OyzzyYmJsbpmCKtVigU4t1332XG009RfLyE/mkBLu9eTW5iyOlojSpSC15RrYs39sax6lgMiQnxfO/qa7j00kv1uihNQgWvlaurq2Pjxo2sXbuW1R98wMcHDwJgYuLxJ2YTTMomlNQRGxXrcFJp6VTw5CsJBXBXFOKpOER0xRGoqz8Zy8zKYsTw4Zx11lkMGjSIxMREh4OKRBafz8dbb73FC/94nsqqas7K9PEf3WrpGB8ZRS/SCl6pz8Xs/V6WHYnF7YniO5ddxlVXXaXXRmlSKngRpqioiLVr17JmzRo+/HAt1dVVAITjMwgkZRNKziEUnwUuXaktn6aCJ1/IWlw1JXjKD9eP1FUVgQ0TExPLoEGDGDbsLM466yxycnI0/VKkGVRVVfHqq6/y2quvUOfzcU67Oi7tWktmK99aIVIKXoXfMPeAl0VHvFjcXJSfzzXXXENGRobT0aQNUMGLYKFQiB07drB27Vo++GAN27YVEA6HMZ5o/AntCSVlE0poV78Fg/bba/NiDq4GwNd5hMNJpEWwFlddOe6qY7grComuLMQGagHo3r0Hw4cP46yzzqJfv37ao0nEQWVlZbz00ku89eabhEIBRneoY0puDakxLf+87bO8sLN+Nd2re9U4nOTrqQka3jkYy/xDcfhDhvETJnDdddfRoUMHp6NJG6KC14ZUVlby0UcfsWbNGlZ/sIbiomMAGHcUgfhMQgntCCW2IxSfCe4oh9OKSLMKh3BVH8ddVYS76hjR1UXYQB0AySkpDB82jKFDhzJ06FDS0tIcDisi/1txcTEvvPACc+fOwUWYcR1rye9cS2J0yz9/iwS+ELz7cSxvfxxPdQBGjx7N1KlT6dKli9PRpA1yvOAZYzoBzwPtgTAw3Vr7d2NMGvAKkAvsBy631p74osdSwftqjh07xpYtW9i8eTMfbdzEgf37sNaCMYTj0gkmZNWXvoR22GjtTyUSUYK++jJXeQxPdRHu6mII11/Dk92xIwPOPJMzzjiDM844Q9MuRVqRwsJCnn32WRa8+y4xbpiYU82kznXEeVT0mkIgDEsOxzLnYDzlPhgxYjjXX/9Devbs6XQ0acNaQsHrAHSw1q43xiQC64BLgOuAUmvtXcaY3wCp1tpff9FjqeB9M1VVVWzdupXNmzezefMWCrYV1K/QCRCbRCA+i1BCFqHEdoRjU7RKp0hrYS3GX4W78hjuqmNEVRdhauo/L3O53fTq2ZMzGwpd//79SU1NdTiwiHxTBw4cYMaMp1m2bDnxUXBR52rG59QRo+1zG0UoDO8djWHWgQRKamHggAHccOON9OvXz+loIs4XvP/zRMbMAh5u+HW+tbawoQQutdb2/qL7quA1rkAgwK5duz41yldZUQ6AiYolEJdZX/gSMjWtU6QlCQdxVZfgri7GXVVEdE0x1lcNgNcbxxln9D85OpeXl0dsrFbZFYlUO3fu5Omnn+KDD9aQHAPf6lLNmOw6PLr0/msJW/jgWDT/3J/AsRpDXl5vfvjDGxgyZIhmOkiL0aIKnjEmF1gO9AcOWmtTTvneCWvt//lY2RhzI3AjQOfOnYccOHCgyXO2VdZaDh8+3DDCt5mPNm7kyOHD//5+XCqBuAzC8ZmEEjIb9uLTO4hIk7IW46vAXVWMu7oYT3UxrtpSCNevpJeRmcnAAQPo37++1OXm5uJ26yN8kbZm8+bNPPXkk2zctInMOMu3c6sY0c6PS53ktFgLm0ujeG1vAgcqXXTLzeX6G25g5MiRKnbS4rSYgmeMSQCWAXdaa/9pjCk7nYJ3Ko3gNb+Kigq2bdvGtm3bKCjYxtaCAqqrKoH6xVuClJLcQgAAFElJREFUcekE4zNPlj4bHe9wYpHWzQTqcFUXN4zOFRNVexwb8AEQExtLXu88+vXrS58+fejbty/p6ekOJxaRlsJay5o1a5j+xOPs2buPLolhLutWxRlpAV118QX2Vrh5dU8CBSc8tG+XxdTrf8i4ceNwacspaaFaRMEzxkQBc4H51tr7Go7tQFM0W51PRvkKCgrYtq2+8O3ZvZtQqH7xBhMTj/+TUb74TELxGZraKfJ5wiFcNSWfKnPU1u8NZYyhS5fcT5W5Ll26aHRORL5UOBxm8eLFPP3kdAqPFdEnNcjl3arpnhx0OlqLUljj4vU9cXxYHENKUiLXfP86Lr74Ym0NIy2e4wXP1I9rP0f9gir/dcrxvwElpyyykmat/dUXPZYKXsvk8/nYvXv3yZG+LVu3cuzo0fpvGoP1phKIzyAcn0UoPoOwN0VTO6XtsRbjqzxZ5jw1xbhqSk+ubJmalkb/fv3o06cPffr0oXfv3sTFaXVbEfn6AoEAc+bM4flnn6GsopLhWT6u6F5DRivfLP2bqgwY3trnZdFhL9HRMVxx5ZVcccUVes2VVqMlFLxRwHvAZuq3SQC4DfgAeBXoDBwELrPWln7RY6ngtR5lZWWnTO0soGDbNmqq6xeBqJ/amUEwPkNTOyVifWqqZXUxUTUlJ/edi46OoXdeb/r17Xuy0GVlZTmcWEQiVU1NDTNnzmTmyy9jwwEm59SQ36WWWI/TyZpXMAyLD8fy5v54aoOG/Isv5rrrrtPen9LqOF7wGpMKXusVDoc5dOgQ27dvp6CggK0FBezds+fTUzu9GYQTNLVTWqFwEFdN6cmFUP73VMvOXbrQr29f+vbtS15eHrm5uXg8bezMSkQcd+zYMaZPn86iRYtIiYXLulZxTntfxC/EYi1sKonipT2JFFYbhgwezE9uvplu3bo5HU3ka1HBkxbr1Kmd27dvZ/OWrRw7Wlj/zU+mdsZ9UvoytGqntAzWYuoqTo7Meaobplra+gkKaWnpJ6+b01RLEWmJtm7dysMPPci27TvomhTimp5V9IjQ6/MKa1y8sDOBzaVR5HTM5sc/uZmzzz5bK2NKq6aCJ61KWVkZO3bsOGURl22nrNrpIRiXUT/Cl1C/KbuN8jqcWCJe0Hdyv7nPWtWyT17eyUVQ8vLyyMzMdDiwiMiX+2Qhlscfe5SSklLGdKzjsm41xEe1/PPB0xEIw7wDXuYciCMqxssPpk7lkksuISpKs4Ok9VPBk1btk1U7Pxnl27p1K7t27yYUbPikMTaJQHwmoYR2hBKytICLfDMn95wrwl1VRFR1Maam/tJgYwy5XbvSv18/8hpKnVa1FJHWrqamhqeffpo3//lPkqIt3+tRybAsf6veVmHbCQ/P7kyisNowduwYfvKTm7WljEQUFTyJOD6fj127drFlyxa2bt3Kxk2bqCgvB8B4ognE/XuEL5SQCW4tdyyfIxzEXX0cd1URrqoioquLsYFaALxxcfWbh/fvT7+G1S011VJEItWOHTu4529/Y9fu3ZyZHuD7varIbGWrbVb6DTN3x/He0Vg6tMvi57f+gmHDhjkdS6TRqeBJxLPWUlhYeLLwbdq8mf379vHJz7SNSyMQn/XvaZ0xibTqjyblazP+6pOjc57qIlzVJSevncvu2JEzzziD/g2FrkuXLtrkVkTalGAwyFtvvcXTTz1JOOjj6h5VnNfB1yreMjcej+KpHUlUBV1ceeVVXHPNNcTGxjodS6RJqOBJm1RdXV1/Dd/WrWzZsoUtW7ZSW1sDgIn24o/LJJTYgVBSe8LeNBW+SNQw3dJTUYi7spDomuPYuvrrOaOiosnrk8cZ/fvTv39/+vbtS0pKisOBRURahqKiIv7yl2ls2PARQzL8TM2rIjG6ZZ4n+kIwc3c8iw7H0i03l9/dfjvdu3d3OpZIk1LBEwFCoRAHDhw4Wfg2btrE0cL6FTtNVCz+hHYNha8D4dgUFb5WyvgqcVcU4qksJLrqKNZXv/dialoaAwcMoF+/fvTv358ePXpomwIRkS8QDod57bXXeOrJ6cS7Q/wwr4Iz0wNOx/qU/ZVuHi9I5ki14fLLL+f6668nJibG6VgiTU4FT+RzFBUVsWHDBjZs2MC69espLioCwETHnSx8waQO2JgkFb4WyvirTxa6qKqj0DBCl5ScwpDBgxg0qP5XTk6OlsQWEfkadu/ezf/8+U/sP3CQiZ1qubJ7DW6HZ69bC/MPxfLK7nhS09L47W2/Y8iQIc6GEmlGKngip6mwsJD169efLHwnShtWT4yJx5/QnmDDCJ+NSXQ4adtlArW4P5lyWXUUausX14lPSGTwoIEnC11ubq4KnYhII/H5fDz++OO8+eab9EsL8JN+lSQ4tJ2CPwTP7ohnxdFYRp1zDr/69a9JSkpyJIuIU1TwRL4Gay2HDh06pfBtoLKivkwQm4g/oQPBlM6EkrLBral+TSYcxl11FE/ZQaIrC6HmBABebxwDBgxgyJDBDBw4kO7du2tBFBGRJvbOO+9w3733kBYd5L/OKKdjfKhZn7/MZ3hwSzK7y91cd911XHvttXrtlzZJBU+kEVhr2b9//8kpnWvXrqO2tgbj8hBIqi97wZRO2Cgto/+NBX14yg/Vl7qKI9igD48nioEDBzBkyBAGDRqka+hERByyZcsWbv/v31FXVc5NfSsYmNE81+Xtq3DzwJYUamw0t932O0aPHt0szyvSEqngiTSBQCDAxo0bWbVqFcvfW0Fx0TEAwgmZBJI7EUzpTNibqmv3TpOpq8BT9jFR5QdxVx4DGyYpOYVR54xk5MiRDBkyBK/X63RMERGh/hr23912G3v27OY/+1Yyop2/SZ9vR5mHezclk5SawbS/3EWPHj2a9PlEWjoVPJEmZq1l3759rFy5khUrV7Jj+/b6b8Qm4k/qRDClE6HE9uByOxu0JbEWV3Vx/Shd+ceYhqmXnbt04dxRoxg5ciR9+vTR1BsRkRaqpqaG3/zm12zevJkf9alkZPumKXnbT3i4d3Myme2yuf+Bv5OZmdkkzyPSmqjgiTSzkpISVq1axcqV77N23VoCfj/GE40/KYdARs/66/ba4sheQ6mLOr6LmPKDWH8tLpeLMwcMYNQ55zBy5Eiys7OdTikiIqeptraW3/7mN2zctJEb8yo5p0PjlrxtJzzctymZdtk53P/A30lPT2/UxxdprVTwRBxUV1fH+vXrWbFiBcuWv0d1VSXEJOBL70Ego2ebWJHTBGrxlOwmpmQ3puYE0dExnHPOSEaNGsWwYcNITIz8fwMRkUhVV1fHbbf9lg3rN/CT/pUMy2qckren3MNfPkqmQ8dO3P/A30lLS2uUxxWJBCp4Ii2Ez+fj/fff5+2332Ht2g+x1hJK6oA/vSfB1NzIWo3ThnGXHyKqeCdR5YfAhunTty8XXXghY8aMIT4+3umEIiLSSHw+H7fe8nN2bi/gvweXkZv4zVbXLPW5uGNtKt7Udjz62OOkpqY2UlKRyKCCJ9ICFRUVMX/+fObOm8exo0cxnmh8qV0JZPQiHJ/RaqdwumrL6qdglu7B+mtITklh8qRJTJ48mS5dujgdT0REmkhpaSk/uvEGwtUl3DGklOTor3de6Q/BnRtSOOqP49HHHqdr166NnFSk9VPBE2nBwuEwmzZt4u2332bJ0qUE/H5sXCp17foTTO8OphUsMmIt7vJDxB7diKuyCJfLxdlnn82FF17I8OHDtZ2BiEgbsWvXLm7+yY/p7K3jt4PK8HzFtzBr4YmCBFYVxXDnndMYOXJk0wQVaeVU8ERaierqapYsWcI/33yTvXv2gDeZ2g4DCaZ1bZlFz1rcFYeJPbIBV1Ux7dq159JLL2HChAm6VkJEpI1avHgxf/rTn/iPrjVc0rX2K9139bFoHt2ayNSpU7n22mubKKFI63e6BU8fsYs4LD4+nvz8fC666CJWrFjB0zOeYf/eZdjCjdRlDySY2rVlTN20FnfFkYZiV0RGZibX/ecvmDRpkkbrRETauLFjx7JixXvMXrqEs7L8dIw/vevxKgOGF3Yn0rt3L773ve81cUqRtkEjeCItTDgcZvny5Tw9YwYfHzyIjUujrsNAgqldHCt67opCYo5swF15lPT0DK699houvPBCoqKiHMkjIiItz4kTJ7j2mqtp7ynnd4PKcZ3GW9YTBfGsLo5j+vQn6d69e9OHFGnFTncErwXO/xJp21wuF+effz7PPvMMt99+OzlpcXj3LCZh22xcNSXNmsX4qojb8S/idrxDusfPz372M15++SWmTJmiciciIp+SmprKT27+f+wq87CiMOZLb7+jzMPKo7FcddV3Ve5EGpFG8ERauFAoxKJFi3j0sccpKy+jLnsIgfb9m3w0z1Oyh7iDq4nxuLj++ql861vfIibmy9+wRUSk7bLW8qMbb6Ds8C7+OqwU9xcMJdz9URKHQunMfOVVYmNjmy+kSCulETyRCOF2u5kwYQLPPfsMo0aOJPbQh8TtnI/xVzfNEwb9xO5dhnfvMvJ6dWfGjKe57LLLVO5ERORLGWO45trvU1RjWF0U/bm321PuYUtpFFdceZXKnUgjU8ETaSWSk5P585//zC9+8Qu8dSUkFszCU7q/UZ/DXXmMxG2ziC7dy3XXXcdDDz5IdnZ2oz6HiIhEtnPOOYduubnMOZBA+HMmis3a7yUxIZ4pU6Y0bziRNkBL34m0IsYY8vPzGTBgAH/805/YvWsxtjC1kbZTsJiaE2RlteP3t/+F/v37N8JjiohIW+Nyubjyu99l2rRp7CzzkJca/NT3S+tcbCyJ5pprv01cXJxDKUUilwqeSCvUqVMnHnv0UV5++WV27NjRaI+bnZ3NddddR3x8fKM9poiItD3nnnsusTHRvH8s5v8UvFXHorHAhAkTnAknEuFU8ERaqaioKG0IKyIiLZLX6+Xc80azcukCzu3g49RlwVYe89Inrzc5OTmO5ROJZCp4IiIiItLoJk6cyIIFC/jzuuT/872fTZzkQCKRtkEFT0REREQa3ZAhQ3jwwQepra391HGPx8OAAQMcSiUS+VTwRERERKTRGWM488wznY4h0uZomwQREREREZEIoYInIiIiIiISIVTwREREREREIoQKnoiIiIiISIRQwRMREREREYkQKngiIiIiIiIRQgVPREREREQkQqjgiYiIiIiIRAgVPBERERERkQihgiciIiIiIhIhVPBEREREREQihAqeiIiIiIhIhFDBExERERERiRAqeCIiIiIiIhGiyQqeMWaGMabIGLPllGNpxpgFxphdDb+nNtXzi4iIiIiItDVNOYL3LDDpfx37DbDIWtsTWNTwtYiIiIiIiDSCJit41trlQOn/OjwFeK7hz88BlzTV84uIiIiIiLQ1nmZ+vnbW2kIAa22hMSbr825ojLkRuLHhyypjzI7mCCgSoTKA406HEBGRNk3vRSLfTJfTuVFzF7zTZq2dDkx3OodIJDDGrLXWDnU6h4iItF16LxJpHs29iuYxY0wHgIbfi5r5+UVERERERCJWcxe82cD3G/78fWBWMz+/iIiIiIhIxGrKbRJeBlYBvY0xh4wx1wN3AeONMbuA8Q1fi0jT03RnERFxmt6LRJqBsdY6nUFEREREREQaQXNP0RQREREREZEmooInIiIiIiISIVTwRCKcMWaSMWaHMWa3MeY3TucREZG2xRgzwxhTZIzZ4nQWkbZABU8kghlj3MAjwGSgL3CVMaavs6lERKSNeRaY5HQIkbZCBU8ksg0Ddltr91pr/cBMYIrDmUREpA2x1i4HSp3OIdJWqOCJRLaOwMenfH2o4ZiIiIiIRCAVPJHIZj7jmPZGEREREYlQKngike0Q0OmUr3OAIw5lEREREZEmpoInEtk+BHoaY7oaY6KBK4HZDmcSERERkSaigicSway1QeBmYD6wDXjVWrvV2VQiItKWGGNeBlYBvY0xh4wx1zudSSSSGWt1OY6IiIiIiEgk0AieiIiIiIhIhFDBExERERERiRAqeCIiIiIiIhFCBU9ERERERCRCqOCJiIiIiIhECBU8ERGJKMaYHGPMLGPMLmPMHmPM3xv2gfyi+9zWXPlERESakgqeiIhEDGOMAf4JvGWt7Qn0AhKAO7/krip4IiISEVTwREQkkowF6qy1zwBYa0PAz4GpxpgfG2Me/uSGxpi5xpjzjTF3AV5jzEfGmBcbvnetMWaTMWajMeYfDce6GGMWNRxfZIzp3HD8WWPMY8aYJcaYvcaY0caYGcaYbcaYZ095vgnGmFXGmPXGmNeMMQnN9q8iIiJthgqeiIhEkn7AulMPWGsrgIOA57PuYK39DVBrrR1orf2eMaYf8DtgrLV2APCzhps+DDxvrT0TeBF48JSHSaW+XP4cmAPc35DlDGPMQGNMBvDfwDhr7WBgLXBLY/yFRURETvWZb3YiIiKtlAHsVzj+WcYCr1trjwNYa0sbjp8N/EfDn/8B3H3KfeZYa60xZjNwzFq7GcAYsxXIBXKAvsDK+lmkRAOrTjOPiIjIaVPBExGRSLIV+PapB4wxSUAnoJxPz1yJ/ZzHON0yeOptfA2/h0/58ydfe4AQsMBae9VpPK6IiMjXpimaIiISSRYBccaYawGMMW7gXuBZYC8w0BjjMsZ0Aoadcr+AMSbqlMe43BiT3vAYaQ3H3weubPjz94AVXyHXauAcY0yPhseMM8b0+qp/ORERkS+jgiciIhHDWmuBS4HLjDG7gJ1AHfWrZK4E9gGbgXuA9afcdTqwyRjzorV2K/Wrbi4zxmwE7mu4zU+BHxhjNgHX8O9r804nVzFwHfByw/1XA3lf9+8pIiLyeUz9e6GIiIiIiIi0dhrBExERERERiRAqeCIiIiIiIhFCBU9ERERERCRCqOCJiIiIiIhECBU8ERERERGRCKGCJyIiIiIiEiFU8ERERERERCLE/wd6HzQeCrQ7UwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 年龄与糖尿病发病率的关系\n",
    "sns.violinplot(x=train.Outcome,y=train.Age)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cm=train.drop(\"Outcome\",axis=1)\n",
    "corrMatt = cm.corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sns.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中看出\n",
    "1、年龄与怀孕次数呈强正相关；\n",
    "2、年龄与三头肌皮肤褶层厚度、2 小时血清胰岛素含量呈负相关；\n",
    "3、怀孕次数与三头肌皮肤褶层厚度、2 小时血清胰岛素含量呈负相关；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 724 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 724 non-null int64\n",
      "Glucose                     724 non-null int64\n",
      "BloodPressure               724 non-null int64\n",
      "SkinThickness               724 non-null int64\n",
      "Insulin                     724 non-null int64\n",
      "BMI                         724 non-null float64\n",
      "DiabetesPedigreeFunction    724 non-null float64\n",
      "Age                         724 non-null int64\n",
      "Outcome                     724 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 76.6 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4、数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X=train.drop(\"Outcome\",axis=1)\n",
    "y=train.Outcome.values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=10, test_size=0.2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 579 entries, 26 to 282\n",
      "Data columns (total 8 columns):\n",
      "Pregnancies                 579 non-null int64\n",
      "Glucose                     579 non-null int64\n",
      "BloodPressure               579 non-null int64\n",
      "SkinThickness               579 non-null int64\n",
      "Insulin                     579 non-null int64\n",
      "BMI                         579 non-null float64\n",
      "DiabetesPedigreeFunction    579 non-null float64\n",
      "Age                         579 non-null int64\n",
      "dtypes: float64(2), int64(6)\n",
      "memory usage: 40.7 KB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5、数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6、模型训练&模型评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1   Logistic Regression正则函数及参数调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L1正则的Logistic Regression的参数调优\n",
    "参数范围选取10^-5到10^5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C: 1e-05 ;     log_loss: 7.8606097696812745\n",
      "C: 0.0001 ;     log_loss: 7.8606097696812745\n",
      "C: 0.001 ;     log_loss: 7.8606097696812745\n",
      "C: 0.01 ;     log_loss: 7.8606097696812745\n",
      "C: 0.1 ;     log_loss: 7.8606097696812745\n",
      "C: 1.0 ;     log_loss: 7.8606097696812745\n",
      "C: 10.0 ;     log_loss: 7.8606097696812745\n",
      "C: 100.0 ;     log_loss: 7.8606097696812745\n",
      "C: 1000.0 ;     log_loss: 7.8606097696812745\n",
      "C: 10000.0 ;     log_loss: 7.8606097696812745\n",
      "C: 100000.0 ;     log_loss: 7.8606097696812745\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "Cs_lr = np.logspace(-5, 5, 11)\n",
    "def fit_grid_lr_L1(Cs,X_train,y_train,X_test,y_test):\n",
    "    for i, oneC in enumerate(Cs):\n",
    "        lr_L1=LogisticRegression(penalty='l1',C=oneC)   #L1正则\n",
    "        lr_L1.fit(X_train,y_train)\n",
    "        y_L1_predict=lr_L1.predict(X_test)\n",
    "        loss=log_loss(y_test,y_predict)   #log_loss评价\n",
    "        print('C:',oneC,';    ','log_loss:',loss)\n",
    "# lr_L1.score(X_test,y_test)\n",
    "fit_grid_lr_L1(Cs_lr,X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L2正则的Logistic Regression\n",
    "参数范围选取10^-5到10^5，优化方法选取了坐标轴下降法和牛顿法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C: 1e-05 ;     log_loss: 7.8606097696812745\n",
      "C: 0.0001 ;     log_loss: 7.8606097696812745\n",
      "C: 0.001 ;     log_loss: 7.8606097696812745\n",
      "C: 0.01 ;     log_loss: 7.8606097696812745\n",
      "C: 0.1 ;     log_loss: 7.8606097696812745\n",
      "C: 1.0 ;     log_loss: 7.8606097696812745\n",
      "C: 10.0 ;     log_loss: 7.8606097696812745\n",
      "C: 100.0 ;     log_loss: 7.8606097696812745\n",
      "C: 1000.0 ;     log_loss: 7.8606097696812745\n",
      "C: 10000.0 ;     log_loss: 7.8606097696812745\n",
      "C: 100000.0 ;     log_loss: 7.8606097696812745\n"
     ]
    }
   ],
   "source": [
    "def fit_grid_lr_L2(Cs,X_train,y_train,X_test,y_test):\n",
    "    for C in Cs:\n",
    "        lr_L1=LogisticRegression(penalty='l2',C=C)  #liblinear坐标轴下降法\n",
    "        lr_L1.fit(X_train,y_train)\n",
    "        y_L1_predict=lr_L1.predict(X_test)  #预测值\n",
    "        loss=log_loss(y_test,y_predict)   #log_loss评价指标\n",
    "        print('C:',C,';    ','log_loss:',loss)\n",
    "fit_grid_lr_L2(Cs_lr,X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C: 1e-05 ;     log_loss: 7.8606097696812745\n",
      "C: 0.0001 ;     log_loss: 7.8606097696812745\n",
      "C: 0.001 ;     log_loss: 7.8606097696812745\n",
      "C: 0.01 ;     log_loss: 7.8606097696812745\n",
      "C: 0.1 ;     log_loss: 7.8606097696812745\n",
      "C: 1.0 ;     log_loss: 7.8606097696812745\n",
      "C: 10.0 ;     log_loss: 7.8606097696812745\n",
      "C: 100.0 ;     log_loss: 7.8606097696812745\n",
      "C: 1000.0 ;     log_loss: 7.8606097696812745\n",
      "C: 10000.0 ;     log_loss: 7.8606097696812745\n",
      "C: 100000.0 ;     log_loss: 7.8606097696812745\n"
     ]
    }
   ],
   "source": [
    "def fit_grid_lr_L2_newton(Cs,X_train,y_train,X_test,y_test):\n",
    "    for C in Cs:\n",
    "        lr_L1=LogisticRegression(penalty='l2',C=C,solver='newton-cg')  #牛顿法\n",
    "        lr_L1.fit(X_train,y_train)\n",
    "        y_L1_predict=lr_L1.predict(X_test)  #预测值\n",
    "        loss=log_loss(y_test,y_predict)   #log_loss评价指标\n",
    "        print('C:',C,';    ','log_loss:',loss)\n",
    "fit_grid_lr_L2_newton(Cs_lr,X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 从以上三个结果来看，L1正则和L2正则效果一样，不同参数的效果也一样，造成的原因可能是数据过少，不具有代表性。接下来用交叉验证的方法来进行参数调优。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 正则化的 Logistic Regression交叉验证参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#需要调优的参数\n",
    "# 请尝试将L1正则和L2正则分开，并配合合适的优化求解算法（slover）\n",
    "#tuned_parameters = {'penalty':['l1','l2'],\n",
    "#                   'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#                   }\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4827501289161031"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-grid.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4924887048555953\n",
      "{'C': 0.1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 交叉验证最佳正则函数选择L2，参数是0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  6.2 线性SVM正则参数调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "线性SVM LinearSVC的需要调整正则超参数包括C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和正则函数penalty（L2/L1） "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_grid_point_Linear(C, X_train, y_train, X_test, y_test):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC2 =  LinearSVC( C = C)\n",
    "    SVC2 = SVC2.fit(X_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC2.score(X_test, y_test)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.7655172413793103\n",
      "accuracy: 0.7724137931034483\n",
      "accuracy: 0.7724137931034483\n",
      "accuracy: 0.7724137931034483\n",
      "accuracy: 0.7724137931034483\n",
      "accuracy: 0.7379310344827587\n",
      "accuracy: 0.7241379310344828\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-3, 3, 7)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份  \n",
    "#penalty_s = ['l1','l2']\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "#    for j, penalty in enumerate(penalty_s):\n",
    "    tmp = fit_grid_point_Linear(oneC, X_train, y_train, X_test, y_test)\n",
    "    accuracy_s.append(tmp)\n",
    "\n",
    "x_axis = np.log10(C_s)\n",
    "plt.plot(x_axis, np.array(accuracy_s), 'b-')\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuracy' )\n",
    "plt.savefig('SVM_Otto.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 结果：从图中可以看出，最优参数是10^-2 , 10^-1 , 1 ,10 这四个参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7793103448275862"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#估计模型性能\n",
    "SVC1=LinearSVC( C = 10)\n",
    "SVC1.fit(X_train,y_train)\n",
    "y_SVC1_predict = SVC1.predict(X_test)\n",
    "SVC1.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.6224113117853385"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用log_loss评估\n",
    "log_loss(y_test,y_SVC1_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Logistic 回归的log_loss是7.8606097696812745，SVC的log_loss是7.6224113117853385， 所以与Logistic 回归的性能比较发现，SVC的性能比Logistic 回归好。原因是SVC优化更好"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3  RBF核SVM正则参数调优\n",
    "\n",
    "RBF核是SVM最常用的核函数。\n",
    "RBF核SVM 的需要调整正则超参数包括C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和核函数的宽度gamma\n",
    "C越小，决策边界越平滑； \n",
    "gamma越小，决策边界越平滑。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_grid_point_RBF(C, gamma, X_train, y_train, X_test, y_test):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC3 =  SVC( C = C, kernel='rbf', gamma = gamma)\n",
    "    SVC3 = SVC3.fit(X_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC3.score(X_test, y_test)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.7379310344827587\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.7862068965517242\n",
      "accuracy: 0.7448275862068966\n",
      "accuracy: 0.6827586206896552\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.7586206896551724\n",
      "accuracy: 0.7379310344827587\n",
      "accuracy: 0.6896551724137931\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.7310344827586207\n",
      "accuracy: 0.696551724137931\n",
      "accuracy: 0.6896551724137931\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n"
     ]
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-2, 2, 5)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份 \n",
    "gamma_s = np.logspace(-2, 2, 5)  \n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, X_train, y_train, X_test, y_test)\n",
    "        accuracy_s.append(tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述运行结果来看，整体来看并无过大差别，但精确度最高的gamma参数在边界上，所以适当调节，找到最优参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.7379310344827587\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6758620689655173\n",
      "accuracy: 0.7862068965517242\n",
      "accuracy: 0.7448275862068966\n",
      "accuracy: 0.6827586206896552\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.7724137931034483\n",
      "accuracy: 0.7586206896551724\n",
      "accuracy: 0.7379310344827587\n",
      "accuracy: 0.6896551724137931\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.7793103448275862\n",
      "accuracy: 0.7310344827586207\n",
      "accuracy: 0.696551724137931\n",
      "accuracy: 0.6896551724137931\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.7655172413793103\n",
      "accuracy: 0.7448275862068966\n",
      "accuracy: 0.6689655172413793\n",
      "accuracy: 0.6896551724137931\n",
      "accuracy: 0.6689655172413793\n"
     ]
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-3, 3, 7)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份 \n",
    "gamma_s = np.logspace(-3, 1, 5)  \n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, X_train, y_train, X_test, y_test)\n",
    "        accuracy_s.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_s1 =np.array(accuracy_s).reshape(len(C_s),len(gamma_s))\n",
    "x_axis = np.log10(C_s)\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    plt.plot(x_axis, np.array(accuracy_s1[:,j]), label = ' Test - log(gamma)' + str(np.log10(gamma)))\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuracy' )\n",
    "plt.savefig('RBF_SVM_Otto.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 从以上图像可以看出，最佳参数是C:1 , gamma:0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7862068965517242"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 以最佳参数来评估模型\n",
    "SVC4 =  SVC( C = 1, kernel='rbf', gamma = 0.01)\n",
    "SVC4 = SVC4.fit(X_train, y_train)\n",
    "    \n",
    "# 在校验集上返回accuracy\n",
    "SVC4.score(X_test, y_test)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.3841963104942945"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_svc4_predict=SVC4.predict(X_test)\n",
    "log_loss(y_test,y_svc4_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 结果：由于rbf的log_loss是7.3841963104942945,而SVM和Logistic的log_loss是7.8606097696812745，所以 rbf的SVM比线性SVM和Logistic回归的性能都好\n"
   ]
  }
 ],
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